Joint Inversion Method Research Articles

Investigating terrestrial water storage changes in Southwest China by integrating GNSS and GRACE/GRACE-FO observations

Study regionSouthwest China. Study focusTerrestrial water storage (TWS) changes estimated from the Global Navigation Satellite System (GNSS) and Gravity Recovery and Climate Experiment missions (GRACE/GRACE-FO) are critical data sets for hydrological studies. To obtain a broader and unified spatiotemporal scale than a single method, we designed two improved joint inversion methods (JIMs). The joint inversion method 1 and 2 (JIM1 and JIM2) are based on GRACE/GRACE-FO-derived virtual stations and 3-degree mascons from Jet Propulsion Laboratory, respectively. We focused on investigating TWS changes in Southwest China using JIM1 and JIM2 New hydrological insight for the regionThe synthetic test showed that JIM1 and JIM2 had lower root-mean-square-errors than a GNSS-Only inversion method (GNSS-IM), with reductions of 22% and 37%, respectively, confirming their feasibility. The results of JIMs using actual GNSS and GRACE/GRACE-FO data showed that the area-averaged JIMs-TWS estimates were consistent with GRACE/GRACE-FO-TWS and GNSS-IM-TWS, but JIM2-TWS was overfit to GNSS-IM-TWS. Then, we utilized JIM1-TWS to investigate hydro-climate relationships and quantify drought events. The results revealed a strong agreement between JIM1-dTWS/dt and precipitation-evapotranspiration-runoff with a correlation coefficient of 0.89. Precipitation contributed the most to TWS changes. Furthermore, three hydrological drought events were detected, with the third event being the most severe, lasting for 20 months, resulting in a total TWS loss of − 501 km3, and being categorized as an exceptional drought.

Joint inversion of formation radial shear-velocity profiles by dipole acoustic logging while drilling

Formation properties near a borehole are frequently altered by mechanical damage from drilling, leading to variations of formation elastic velocities in the radial direction. Inversions of radial shear profiles are necessary for a real-time evaluation of the drilling conditions. In this study, we investigate acoustic wave propagation in a logging-while-drilling (LWD) model excited by a dipole transducer in cylindrically layered media. It is revealed that the dispersion characteristics of the dipole waves of the LWD are sensitive to the radial formation velocity variation. Accordingly, we develop a joint inversion method that evaluates radial shear-velocity profiles using the dipole modes of the first and second orders. The propagation of these two modes is strongly influenced by formation alterations, but the inversion results obtained using a single dipole mode indicate ambiguity caused by the nonuniqueness problem. Through our joint inversion, the additional second-order dipole wave can effectively help address the nonuniqueness issue in dipole acoustic logging, although its dispersion features differ in fast and slow formations. It is validated that the joint inversion has considerably improved convergence and accuracy in evaluating the thickness and shear velocity of the altered zone, compared with the estimation by a single acoustic mode. Furthermore, we develop numerical results based on synthetic logging data to demonstrate the feasibility of this inversion in a continuously varying profile, regardless of whether the formation surrounding the borehole is fast or slow. Even when strong drilling noises of low frequencies are added to the receiver signal arrays, the means of joint inversion can remain stable, and the shear-velocity profiles can be evaluated with good precision.

Calibration and Sensitivity Analysis of Macro and Meso Parameters of Discrete Element Model for Coal Measure Soil

The orthogonal test sequence of the meso parameters of the discrete element model was designed by using the orthogonal test method. The calculation method of directly determining the meso parameters from the macro mechanical parameters was established by using the test simulation calculation results and the multi factor nonlinear joint inversion method. The proposed method was applied to the discrete element numerical simulation of triaxial test of coal measure soil. The results show that the numerical simulation results are in good agreement with the stress-strain curves of the triaxial test of coal bearing soil. Through error analysis, it is found that the maximum error of the partial stress peak is 5.4%, the error of the cohesion is 2%, the error of the internal friction angle is 7%, and the error is all within 10%. It verifies that the method for rapid determination of the meso parameters proposed in this paper is feasible, and provides a new idea for the calibration of the meso parameters in the discrete element numerical simulation method.

A S-wave Spectral Decomposition Hydrocarbon Detection Method Based on P-wave and S-wave Source and Its Application

S-wave source vector exploration is a promising technology in future seismic exploration. Conventional P-wave and S-wave joint inversion methods for hydrocarbon detection are limited in some aspects. First, P-wave and S-wave joint inversion is mainly based on the converted wave theory, but no theoretical support for S-wave generated by S-wave source. Second, the inversion method is complex and difficult to operate. Because P-wave and S-wave from S-wave source should accurately match with each other, without an effective approach, it has to follow the method of P-wave matching with converted wave, so it is not helpful to extensive application. This paper proposes a comprehensive s-wave spectral decomposition hydrocarbon detection method based on p-wave and s-wave source. First, the velocity ratio of P-wave to S-wave (Vp/Vs ratio) is derived to be equal to the frequency notch period ratio of P-wave to S-wave, and approximately equal to the dominant frequency ratio of P-wave to S-wave. Second, spectral decomposition is conducted at the target layer to obtain the tuning frequency spectra of P-wave and S-wave seismic data, respectively, and the dominant frequencies of P-wave and S-wave are automatically identified. Third, the Vp/Vs ratio and Poisson's ratio are calculated according to the dominant frequency ratio of P-wave to S-wave for final hydrocarbon detection. This method is independent on well data and accurate matching of P-wave with S-wave data, it is simple, quick and efficient. It’s widely applicable in any areas more or less explored. Application in an oilfield in western China has obtained effective results.

Joint inversion of gravity and vertical gradient data based on modified structural similarity index for the structural and petrophysical consistency constraint

Joint inversion is one of the most effective methods for reducing non-uniqueness for geophysical inversion. The current joint inversion methods can be divided into the structural consistency constraint and petrophysical consistency constraint methods, which are mutually independent. Currently, there is a need for joint inversion methods that can comprehensively consider the structural consistency constraints and petrophysical consistency constraints. This paper develops the structural similarity index (SSIM) as a new structural and petrophysical consistency constraint for the joint inversion of gravity and vertical gradient data. The SSIM constraint is in the form of a fraction, which may have analytical singularities. Therefore, converting the fractional form to the subtractive form can solve the problem of analytic singularity and finally form a modified structural consistency index of the joint inversion, which enhances the stability of the SSIM constraint applied to the joint inversion. Compared to the reconstructed results from the cross-gradient inversion, the proposed method presents good performance and stability. The SSIM algorithm is a new joint inversion method for petrophysical and structural constraints. It can promote the consistency of the recovered models from the distribution and the structure of the physical property values. Then, applications to synthetic data illustrate that the algorithm proposed in this paper can well process the synthetic data and acquire good reconstructed results.

Seismic evidence for slab detachment beneath the Taiwan Orogen

To clarify the deep structure of the Taiwan Orogen formed by the collision between the Eurasian and the Philippine Sea plates, we determine a robust 3-D shear-wave velocity (Vs) model of the crust and upper mantle beneath the Taiwan Island and its surrounding regions by applying a novel joint inversion method to local and teleseismic P and S wave travel times, Rayleigh-wave phase velocities and S-wave receiver functions. A notable new feature revealed by our model is the morphologic variation of the subducting Eurasian slab along the orogen belt. Beneath South Taiwan, the subducting Eurasian slab is imaged as a continuous high-Vs zone down to ∼400 km depth with an obvious dip angle change at ∼70 km depth. Beneath Central Taiwan, the Eurasian slab is imaged as intermittent high-Vs anomalies in the upper mantle, whereas the Eurasian slab exhibits as a sheet-like high-Vs zone with a low dip angle down to only ∼100 km depth beneath North Taiwan. These results suggest that parts of the subducting Eurasian slab have been detached beneath the Philippine Sea east off the Taiwan Island, which may result in the remnant slab rebounding to underplate the upper plate and so promote mountain building on the Taiwan Island.

Deep Learning Joint Inversion of Electrical Data for Ahead-Prospecting in Tunneling

Water inrush has become one of the bottlenecks restricting tunnel construction. Among various advanced forecasting techniques, the direct current method is more cost-effective and sensitive to water-bearing structures. It has been widely used in exploring water inrush disasters in practical engineering. Although traditional resistivity linear inversion methods are reasonably practical, they usually suffer from volume effects and cannot accurately locate the location and morphology of water-bearing bodies. Therefore, nonlinear techniques such as deep learning have recently become popular to directly approximate the inversion function by learning the mapping of apparent resistivity data to the geoelectric model. This work presents a novel deep learning-based electrical approach that combines resistivity and polarizability to estimate water-bearing location and morphology. Specifically, we design an encoder-decoder network. A shared encoder extracts features from the input data, two encoders output resistivity, and polarizability models, respectively, and fine-tuned collinear regularization for both outputs reduces solutions’ multiplicity. Compared with traditional linear inversion methods and independent parameter inversion, our proposed joint inversion method shows superiority in locating and delineating anomalous bodies.

Probabilistic approach to Gramian inversion of multiphysics data

We consider a probabilistic approach to the joint inversion of multiphysics data based on Gramian constraints. The multiphysics geophysical survey represents the most effective technique for geophysical exploration because different physical data reflect distinct physical properties of the various components of the geological system. By joint inversion of the multiphysics data, one can produce enhanced subsurface images of the physical properties distribution, which improves our ability to explore natural resources. One powerful method of joint inversion is based on Gramian constraints. This technique enforces the relationships between different model parameters during the inversion process. We demonstrate that the Gramian can be interpreted as a determinant of the covariance matrix between different physical models representing the subsurface geology in the framework of the probabilistic approach to inversion theory. This interpretation opens the way to use all the power of the modern probability theory and statistics in developing novel methods for joint inversion of the multiphysics data. We apply the developed joint inversion methodology to inversion of gravity gradiometry and magnetic data in the Nordkapp Basin, Barents Sea to image salt diapirs.

Focal Mechanisms and Stress Field Characteristics of Microearthquakes in Baihetan Reservoir in the Downstream Area of Jinsha River

The Baihetan Reservoir was impounded on 6 April 2021, after which the water level rose significantly. Notably, after one week of impoundment, microseismic activities were prominent around the reservoir area, which was highly associated with the water level change. From 6 April 2021 to 31 December 2021, over 7000 microearthquakes were recorded by the seismic stations in the vicinity of the reservoir, including 12 ML > 3 events. The maximum was the 21 December 2021 ML3.9 earthquake in Qiluogou town, Sichuan. The post-impoundment seismic events were clustered in Hulukou town in the Qiaojia Basin, with an overall “Y-shaped” pattern. In this study, taking advantage of the high-frequency waveform matching approach, the pre- and post-impoundment focal mechanism solutions totaling 207 ML > 2 earthquakes are successfully obtained. The impoundment-induced stress change is analyzed, and the iterative joint inversion method is used to invert the stress field. Major results and conclusions include the following: (1) After impoundment, the number of normal fault earthquakes remarkably increased in the reservoir area; (2) Impoundment has led to a vertical compressive stress field and horizontal tensile stress field in the area where microearthquakes occurred. It is necessary to pay close attention to possible moderate-to-strong earthquakes in the future.

Fast 3D joint inversion of gravity and magnetic data based on cross gradient constraint

The gravity and magnetic data can be adopted to interpret the internal structure of the Earth. To improve the calculation efficiency during the inversion process and the accuracy and reliability of the reconstructed physical property models, the triple strategy is adopted in this paper to develop a fast cross-gradient joint inversion for gravity and magnetic data. The cross-gradient constraint contains solving the gradients of the physical property models and performing the cross-product calculation of their gradients. The sparse matrices are first obtained by calculating the gradients of the physical property models derived from the first-order finite difference. Then, the triple method is applied to optimize the storages and the calculations related to the gradients of the physical property models. Therefore, the storage compression amount of the calculations related to the gradients of the physical property models and the cross-gradient constraint are reduced to one-fold of the number of grid cells at least, and the compression ratio increases with the increase of the number of grid cells. The test results from the synthetic data and field data prove that the structural coupling is achieved by using the fast cross-gradient joint inversion method to effectively reduce the multiplicity of solutions and improve the computing efficiency.

Electrical–Elastic Joint Inversion Method for Fracture Characterization in Anisotropic Media

Electrical–Elastic Joint Inversion Method for Fracture Characterization in Anisotropic Media

Joint Inversion Method of Gravity and Magnetic Analytic Signal Data With Adaptive Unstructured Tetrahedral Subdivision

Joint Inversion Method of Gravity and Magnetic Analytic Signal Data With Adaptive Unstructured Tetrahedral Subdivision

3D velocity and anisotropy of the southeastern Tibetan plateau extracted by joint inversion of wave gradiometry, ambient noise, and receiver function

With the deformation pattern of the southeastern Tibetan Plateau remaining controversial, its anisotropic structure and the three-dimensional (3D) velocity model are powerful tools for understanding this issue. The surface wave dispersion curve (DC) and receiver function (RF) are sensitive to absolute velocity and elasticity contrasts, respectively. In this work, we propose a new method for the joint inversion of 3D shear velocity (Vs) and anisotropy using the datasets of RFs and DCs, which are obtained by combining the wave gradiometry method and ambient noise tomography. Because DCs and RFs are divided into back-azimuth bins, and both datasets in each bin are jointly inverted to obtain velocity in the corresponding direction, the method is called the azimuth-dependent joint inversion method. By applying this new method to the Temporary West Sichuan Array, we obtained a high-resolution 3D Vs model and anisotropic structure of the southeastern Tibetan Plateau. In our model, the vertically consistent fast propagation direction in the Songpan-Ganzi block (SGB) and the north Chuandian block (NCDB) suggest that the deformation in this region is largely coupled vertically. Our model provides anisotropic evidence of the Yangtze crystalline basement extending to the SGB. The low-velocity zone (LVZ) was much thicker and more prominent in the NCDB than in the SGD. Anisotropy in the middle lower crust of the NCDB does not show any sign of an LVZ flowing from the SGB or the Qiangtang block to the NCDB. These observations are contrary to those of the crust flow model, suggesting that the LVZ was generated locally. The mushroom-shaped high-velocity structure beneath the southern Chuandian block and boundary fault zone may be a relic of the Emeishan paleo-mantle plume.

Joint inversion of VES and Rayleigh wave data based on improved DE algorithm for near surface exploration

PurposeVertical electrical sounding (VES) and Rayleigh wave exploration are widely used in the exploration of near-surface structure, but both have limitations. This study aims to make full use of the advantages of the two methods, reduce the multiple solutions of single inversion and improve the accuracy of the inversion. Thus, a nonlinear joint inversion method of VES and Rayleigh wave exploration based on improved differential evolution (DE) algorithm was proposed.Design/methodology/approachBased on the DE algorithm, a new initialization strategy was proposed. Then, taking AK-type with high-velocity interlayer model and HA-type with low-velocity interlayer model near the surface as examples, the inversion results of different methods were compared and analyzed. Then, the proposed method was applied to the field data in Chengde, Hebei Province, China. The stratum structure was accurately depicted and verified by drilling.FindingsThe synthetic data and field data results showed that the joint inversion of VES and Rayleigh wave data based on the improved DE algorithm can effectively improve the interpretation accuracy of the single-method inversion and had strong stability and large generalizable ability in near-surface engineering problems.Originality/valueA joint inversion method of VES and Rayleigh wave data based on improved DE algorithm is proposed, which can improve the accuracy of single-method inversion.

Joint inversion method of formation shear-wave anisotropy from logging-while-drilling acoustic data

Joint inversion method of formation shear-wave anisotropy from logging-while-drilling acoustic data

Joint Inversion of Body Wave Arrival Times, Surface Wave Dispersion Data and Receiver Functions: Method and Application to South China

AbstractWe have developed a new joint inversion method that incorporates body wave arrival times, surface wave dispersion and receiver functions to simultaneously update earthquake locations and constrain three‐dimensional P‐wave (Vp) and S‐wave velocity (Vs) models. Due to complementary sensitivities of the three types of data, the proposed joint inversion algorithm can reduce the intrinsic non‐uniqueness of inversions using fewer types of data and better determine smooth velocity variations and velocity discontinuities. Synthetic tests demonstrate the advantages of this new joint inversion algorithm in resolving velocity structures, especially in constraining velocity gradients across the Moho interface. We have applied the proposed joint inversion algorithm to image the lithosphere velocity structure of south China. The inverted Vp and Vs models fit body wave arrival times, surface wave dispersion and receiver functions well. We further analyzed the distribution of Vs gradients across the Moho interface in detail, which helps us better understand tectonics in south China.

CO2-Containing Reservoir Evaluation Based on the Joint Inversion of Nuclear Logs and Resistivity Logs: A Case Study of Ultrahigh-Temperature and High-Pressure Gas Reservoirs in the Yinggehai Basin, China.

Due to the unique characteristics of reservoirs in the Yinggehai Basin in the South China Sea, such as high temperature and high pressure (HPHT), low porosity, low permeability, complex pore structure, and high lime content, the log responses of these reservoirs have very complex characteristics, which makes it difficult to evaluate reservoir parameters accurately. In addition, most reservoirs in Ledong Block of the Yinggehai Basin in the South China Sea contain CO2, posing great difficulties for subsequent exploration and development. Accurate evaluation of CO2 layers is of paramount importance for the development of oil and gas fields. In this study, we used a method for the joint inversion of multiple well logs to evaluate the reservoirs and determine CO2 saturation level and other formation parameters. We optimized the joint inversion model based on the characteristics of the reservoirs in the Yinggehai Basin and adjusted the forward simulation model to consider the effects of high temperature and high pressure on gas density. In view of high lime content in the formations in this area, we adjusted the resistivity forward simulation model to consider the effect of lime content. The inversion results show that the values of porosity, permeability, and water saturation level obtained through inversion are largely consistent with the core data. The CO2 saturation level determined through joint inversion is 22%, which represents a deviation of less than 10% from the drilling system testing (DST) result, indicating that the joint inversion method is accurate. The error in the water saturation level determined through the joint inversion method is smaller than that in the calculated results from conventional multimineral inversion models. We performed forward simulation of the results calculated with the joint inversion method and compared the results of forward simulation with actual log curves. For the sandstone interval, the results of forward simulation are largely consistent with the actual log curves, indicating that the joint inversion method is accurate. In summary, the method presented in this paper can accurately determine reservoir parameters and provide strong support for the exploration and development of oil and gas fields in the Yinggehai Basin in the South China Sea.

A Joint Method for Wave and Wind Field Parameter Inversion Combining SAR with Wave Spectrometer Data

Synthetic aperture radar (SAR) and wave spectrometer are common methods for observing the ocean wind field and waves on the sea surface, but both have limitations. Due to the influence of velocity bunching modulation, SAR wave observation is limited by the azimuth cut-off phenomenon. Meanwhile, SAR relies on an external wind direction source, so it is difficult for SAR to observe wind fields independently. There is no azimuthal cut-off phenomenon when the spectrometer observes the sea surface, but its azimuth resolution is much lower than SAR. Combining the above characteristics, the joint inversion of SAR and wave spectrometer for sea-state parameters becomes possible. In this paper, a joint method for wind field and wave parameter inversion combining SAR with spectrometer data is proposed. In this method for wave parameter inversion, the wave spectrum of the spectrometer was used as a first-guess spectrum of SAR wave spectrum inversion, the fit wave spectrum obtained by joint inversion and the modified wave spectrum of the spectrometer were fused to form a new spectrum, and the wave parameters were calculated. For wind field parameter inversion, wind direction was obtained using a wave spectrometer, and was used as the input of the SAR wind field inversion. Wind speed was obtained using the CMOD5.N method. Collocated data from Sentinel-1 SAR, the wave spectrometer, the National Data Buoy Center (NDBC) buoy, and the European Centre for Medium-Range Weather Forecasting (ECMWF) are used to verify the proposed method. Sea parameters retrieved from the spectrometer and SAR are compared to the buoy and ECMWF data. The results show that the root mean square errors of significant wave height, mean wave period, wind direction and wind speed are 0.37 m, 1.02 s, 22.7° and 1.06 m/s with ECMWF data, and 0.35 m, 0.78 s, 18.22° and 0.92 m/s with buoy data, respectively. By comparing the inversion results with the L2 products of SAR and SWIM, it can be concluded that the inversion accuracy of the joint method is higher in the middle and low sea conditions. Therefore, the joint inversion method for wind field and wave parameters proposed in this paper has good results, which verifies the accuracy of the joint inversion method.

Joint Inversion of Evaporation Duct Based on Radar Sea Clutter and Target Echo Using Deep Learning

Tropospheric duct is an anomalous atmospheric phenomenon over the sea surface that seriously affects the normal operation and performance evaluation of electromagnetic communication equipment at sea. Therefore, achieving precise sensing of tropospheric duct is of profound significance for the propagation of electromagnetic signals. The approach of inverting atmospheric refractivity from easily measurable radar sea clutter is also known as the refractivity from clutter (RFC) technique. However, inversion precision of the conventional RFC technique is low in the low-altitude evaporation duct environment. Due to the weak attenuation of the over-the-horizon target signal as it passes through the tropospheric duct, its strength is much stronger than that of sea clutter. Therefore, this study proposes a new method for the joint inversion of evaporation duct height (EDH) based on sea clutter and target echo by combining deep learning. By testing the inversion performance and noise immunity of the new joint inversion method, the experimental results show that the mean error RMSE and MAE of the new method proposed in this paper are reduced by 41.2% and 40.3%, respectively, compared with the conventional method in the EDH range from 0 to 40 m. In particular, the RMSE and MAE in the EDH range from 0 to 16.7 m are reduced by 54.2% and 56.4%, respectively, compared with the conventional method. It shows that the target signal is more sensitive to the lower evaporation duct, which obviously enhances the inversion precision of the lower evaporation duct and has effectively improved the weak practicality of the conventional RFC technique.

Physically Constrained 2D Joint Inversion of Surface and Body Wave Tomography

Joint inversion of different geophysical methods is a powerful tool to overcome the limitations of individual inversions. Body wave tomography is used to obtain P-wave velocity models by inversion of P-wave travel times. Surface wave tomography is used to obtain S-wave velocity models through inversion of the dispersion curves data. Both methods have inherent limitations. We focus on the joint body and surface waves tomography inversion to reduce the limitations of each individual inversion. In our joint inversion scheme, the Poisson ratio was used as the link between P-wave and S-wave velocities, and the same geometry was imposed on the final velocity models. The joint inversion algorithm was applied to a 2D synthetic dataset and then to two 2D field datasets. We compare the obtained velocity models from individual inversions and the joint inversion. We show that the proposed joint inversion method not only produces superior velocity models but also generates physically more meaningful and accurate Poisson ratio models.